论文标题
使用反概率加权方法的右审核预测变量回归
Regression with a right-censored predictor, using inverse probability weighting methods
论文作者
论文摘要
在一项纵向研究中，关键变量的度量可能是由于辍学，随访的损失或在感兴趣的发生出现之前发生的研究的提前终止而导致的。在本文中，我们主要关注具有随机审查预测因子的回归模型的实现。我们特别研究了当右键的预测因子是右审查时，在广义线性模型（GLM）中使用反概率加权方法以调整审查。为了提高完整案例分析的性能并防止选择偏见，我们考虑了三种不同的加权方案：逆审查概率权重，Kaplan-Meier权重和COX比例危害权重。我们使用蒙特卡洛模拟研究来评估和比较不同加权估计方法的经验特性。最后，我们将这些方法应用于弗雷明汉心脏研究数据，作为一个说明性的例子，以估算临床诊断的心血管事件的发病年龄与吸烟者中低密度脂蛋白（LDL）之间的关系。
In a longitudinal study, measures of key variables might be incomplete or partially recorded due to drop-out, loss to follow-up, or early termination of the study occurring before the advent of the event of interest. In this paper, we focus primarily on the implementation of a regression model with a randomly censored predictor. We examine, particularly, the use of inverse probability weighting methods in a generalized linear model (GLM), when the predictor of interest is right-censored, to adjust for censoring. To improve the performance of the complete-case analysis and prevent selection bias, we consider three different weighting schemes: inverse censoring probability weights, Kaplan-Meier weights, and Cox proportional hazards weights. We use Monte Carlo simulation studies to evaluate and compare the empirical properties of different weighting estimation methods. Finally, we apply these methods to the Framingham Heart Study data as an illustrative example to estimate the relationship between age of onset of a clinically diagnosed cardiovascular event and low-density lipoprotein (LDL) among cigarette smokers.
